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- 1. DATA VISUALIZATION: INTRODUCTION TOTABLEAU D-VELOP WORKSHOP SERIES – Summer 2021 Trevor Bonjour 1
- 2. D-VELOP WORKSHOP SERIES– Summer 2021 2 Jun 9 • Data Visualization: ggplot2 Jun 16 • Data Visualization using Python: Matplotlib and Seaborn Jun 23 • Exploratory Data Analysis in R July 7 • Data Visualization using Python: Bokeh (Interactive Plots) July 14 • Exploring and Visualizing Time Series Data July 21 • Data Visualization: Introduction to Tableau
- 3. What will wecover today? § Motivation § What is Tableau? § Tableau Workflow § Important Components § Learn by Doing 3
- 4. Visualization Objectives § Recordinformation § Analyze data to support reasoning § Confirm hypotheses § Communicate ideas to others 4
- 5. Why Visualize To recordinformation 5
- 6. Why Visualize To pointout interesting things 6
- 7. Why Visualize To pointout interesting things 7
- 8. Why Visualize To communicateinformation 8
- 9. Why Visualize To analyzedata 9 2020 US Elections (NYTimes)
- 10. Why Visualize To analyzedata 10 2020 US Elections (NYTimes)
- 11. Tableau Workflow 11 Connect • Data source Analyze •Visualize data in Workspace Share • Dashboard or Story
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- 14. Data Sources Types 14 Spreadsheets •Excel or csv file Relational Databases • MySQL or Oracle Cloud Data • AWS or Microsoft Azure Other Sources • Spatial Files or R
- 15. Data Field 15 A field,also known as a column, is a single piece of information from a record in a data set. • Qualitative Field (Dimensions) • Describes or Categorizes Data • What, when or who • Slices the quantitative data • Quantitative Field (Measures) • Numerical Data • Provides measurement for qualitative category • Can be used in calculations
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- 19. 19 Data Field Dimensions Measures • Bydefault, aggregated by SUM • Can be aggregated as average, median, count, or count distinct.
- 20. 20 Data Field Dimensions Measures • Bydefault, aggregated by SUM • Can be aggregated as average, median, count, or count distinct. • Break down the aggregated total into smaller totals by category.
- 21. 21 Data Types Text orString Values Discrete Date/Time Discrete Date Geographic field - State or Zip Code Continuous Numeric Value Calculated Field
- 22. 22 Chart Types Line —View trends in data over time. Examples: Stock price change over a five-year period or website page views during a month. Bar — Compare data across categories. Examples: Volume of shirts in different sizes, or percent of spending by department. Heat Map — Show the relationship between two factors. Examples: Segment analysis of target market, or sales leads by individual rep.
- 23. 23 Chart Types Highlight Table— Shows detailed information on heat maps. Examples: The percent of a market for different segments, or sales numbers in a region. Treemap — Show hierarchical data as a proportion of a whole. Examples: Storage usage across computer machines, comparing fiscal budgets between years. Gantt — Show duration over time. Examples: Project timeline, duration of a machine’s use, availability of players on a team.
- 24. 24 Chart Types Scatterplot —Investigate relationships between quantitative values. Examples: Male versus female likelihood of having lung cancer at different ages Bullet — Evaluate performance of a metric against a goal. Examples: Sales quota assessment, performan ce spectrum (great/good/poor). Histogram — Understand the distribution of your data. Examples: Number of customers by company size, student performance on an exam, frequency of a product defect.
- 25. 25 Chart Types Box-and-Whisker — Showthe distribution of a set of a data. Examples: Understand ing your data briefly, seeing how data is skewed towards one end, identifying outliers in your data. Symbol maps — Use for totals rather than rates. Be careful, as small differences will be hard to see. Examples: Number of customers in different geographies. Area maps — Use for rates rather than totals. Use sensible base geography. Examples: Rates of internet-usage in certain geographies, house prices in different neighborhoods.
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- 44. 44 Share • Saved File •.twb or .twbx • Publish to Tableau Server (Secure) • Publish to Tableau Public (Unsecure)
- 45. LEARN BY DOING 45 Toaccess the videos and material from the workshop series please visit: https://guides.lib.purdue.edu/d-velop